Accurate Patient Alignment without Unnecessary Imaging Dose via Synthesizing Patient-specific 3D CT Images from 2D kV Images

• URL: http://arxiv.org/abs/2405.19338v1
• Date: Mon, 1 Apr 2024 19:55:03 GMT
• Title: Accurate Patient Alignment without Unnecessary Imaging Dose via Synthesizing Patient-specific 3D CT Images from 2D kV Images
• Authors: Yuzhen Ding, Jason M. Holmes, Hongying Feng, Baoxin Li, Lisa A. McGee, Jean-Claude M. Rwigema, Sujay A. Vora, Daniel J. Ma, Robert L. Foote, Samir H. Patel, Wei Liu,
• Abstract summary: Tumor visibility is constrained due to the projection of patient's anatomy onto a 2D plane. In treatment room with 3D-OBI such as cone beam CT(CBCT), the field of view(FOV) of CBCT is limited with unnecessarily high imaging dose. We propose a dual-models framework built with hierarchical ViT blocks to reconstruct 3D CT from kV images obtained at the treatment position.
• Score: 10.538839084727975
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In radiotherapy, 2D orthogonally projected kV images are used for patient alignment when 3D-on-board imaging(OBI) unavailable. But tumor visibility is constrained due to the projection of patient's anatomy onto a 2D plane, potentially leading to substantial setup errors. In treatment room with 3D-OBI such as cone beam CT(CBCT), the field of view(FOV) of CBCT is limited with unnecessarily high imaging dose, thus unfavorable for pediatric patients. A solution to this dilemma is to reconstruct 3D CT from kV images obtained at the treatment position. Here, we propose a dual-models framework built with hierarchical ViT blocks. Unlike a proof-of-concept approach, our framework considers kV images as the solo input and can synthesize accurate, full-size 3D CT in real time(within milliseconds). We demonstrate the feasibility of the proposed approach on 10 patients with head and neck (H&N) cancer using image quality(MAE: <45HU), dosimetrical accuracy(Gamma passing rate (2%/2mm/10%)>97%) and patient position uncertainty(shift error: <0.4mm). The proposed framework can generate accurate 3D CT faithfully mirroring real-time patient position, thus significantly improving patient setup accuracy, keeping imaging dose minimum, and maintaining treatment veracity.

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